The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this functionality. The main strengths of Weka are that it is
freely available under the GNU General Public License,
very portable because it is fully implemented in the Java programming language and thus runs on almost any computing platform,
contains a comprehensive collection of data preprocessing and modeling techniques, and
is easy to use by a novice due to the graphical user interfaces it contains.

Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka's techniques are predicated on the assumption that the data is available as a single flat file or relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported). Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka. Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.

Weka's main user interface is the Explorer, but essentially the same functionality can be accessed through the component-based Knowledge Flow interface and from the command line. There is also the Experimenter, which allows the systematic comparison of the predictive performance of Weka's machine learning algorithms on a collection of datasets.

The Explorer interface has several panels that give access to the main components of the workbench. The Preprocess panel has facilities for importing data from a database, a CSV file, etc., and for preprocessing this data using a so-called filtering algorithm. These filters can be used to transform the data (e.g., turning numeric attributes into discrete ones) and make it possible to delete instances and attributes according to specific criteria. The Classify panel enables the user to apply classification and regression algorithms (indiscriminately called classifiers in Weka) to the resulting dataset, to estimate the accuracy of the resulting predictive model, and to visualize erroneous predictions, ROC curves, etc., or the model itself (if the model is amenable to visualization like, e.g., a decision tree). Weka contains many of the latest sophisticated methods, such as support vector machines, gaussian processes, random forests, but also classic methods like C4.5, ANNs, bagging, boosting, etc. The Associate panel provides access to association rule learners that attempt to identify all important interrelationships between attributes in the data. The Cluster panel gives access to the clustering techniques in Weka, e.g., the simple k-means algorithm. There is also an implementation of the expectation maximization algorithm for learning a mixture of normal distributions. The next panel, Select attributes provides algorithms for identifying the most predictive attributes in a dataset. The last panel, Visualize, shows a scatter plot matrix, where individual scatter plots can be selected and enlarged, and analyzed further using various selection operators.

Bagging and RandomForest are now faster if the base learner is a WeightedInstancesHandler

Speed-ups for REPTree and other classes that use entropy calculations

Many other code improvements and speed-ups

Additional statistics available in the output of LinearRegression and SimpleLinearRegression. Contributed by Chris Meyer

Reduced memory consumption in BayesNet

Improvements to the package manager: load status of individual packages can now be toggled to prevent a package from loading; "Available" button now displays the latest version of all available packages that are compatible with the base version of Weka

RandomizableFilteredClassifier

Canopy clusterer

ImageViewer KnowledgeFlow component

PMML export support for Logistic. Infrastructure and changes contributed by David Person